Aerosol multi-parameter dynamic monitoring system and mass transfer source item correction method based on experiment-simulation fusion

Abstract

The invention relates to an aerosol multi-parameter dynamic monitoring system and a mass transfer source item correction method based on experiment-simulation fusion, and the aerosol multi-parameter dynamic monitoring system comprises a condensation nucleus control device, a flue gas simulation device and a multi-measuring-point filler type absorption tower, the condensation nucleus control device is used for generating monodisperse aerosol condensation nucleuses with single particle size; the flue gas simulation device is used for generating simulation flue gas with controllable components and flow based on the monodisperse aerosol condensation nucleus; the multi-measuring-point packing type absorption tower is provided with at least three aerosol sampling measuring points at intervals along the height direction of the tower body; the particle information acquisition system comprises a constant-speed sampling gun and an ELPI measuring instrument. Compared with the prior art, the method has the advantages that the mass transfer coefficient of the secondary aerosol can be accurately obtained, and the dynamic correction of the source item in numerical simulation is realized, so that the simulation precision and engineering adaptability of the carbon capture system in the aspects of absorbent loss and aerosol escape control are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon capture and storage, and particularly relates to an aerosol multi-parameter dynamic monitoring system and a mass transfer source term correction method based on experiment-simulation fusion. Background Art

[0002] Due to its high maturity and high absorption efficiency, the organic amine method has been widely used to capture CO2 after fossil fuel combustion. However, in actual operation, a large amount of secondary aerosol is often generated within the absorption tower. In particular, under conditions of high-speed gas-liquid interface disturbance and solvent atomization, amine-carrying aerosols with particle sizes less than 3 microns are produced. These aerosols not only carry large amounts of organic amine solvent with them, leading to increased operating costs, but also pose environmental pollution and corrosion risks, seriously affecting the economic viability and stability of the carbon capture system.

[0003] In organic amine carbon capture technology, the formation and mass transfer characteristics of secondary aerosols have a direct impact on system performance. Reference patent CN118616023A proposes a method for preparing a MOF-based porous solid-state carbon capture adsorbent. Although this method can effectively capture CO2, it does not conduct in-depth research on the formation of secondary aerosols and their impact on system performance. Another reference patent, CN109918770A, focuses on the aerosol removal effect of rainfall. It studies the scale spectrum distribution of aerosols and the raindrop capture efficiency, but does not involve the dynamic mass transfer source term correction of aerosols in the carbon capture process. The above patents fail to fully address the lack of research on the mass transfer behavior of secondary aerosols, especially in organic amine carbon capture technology, where there are obvious technical gaps in the dynamic monitoring of secondary aerosols and the construction of source term correction models.

[0004] Existing research has largely focused on the liquid or gas film control mechanisms in the primary mass transfer process, but the mass transfer behavior of secondary aerosols is understudied. In particular, model construction often suffers from issues such as deviating mass transfer coefficients and insufficient precision in source term representation. Furthermore, due to the wide size distribution, complex interfacial morphology, and significant dynamic behavior of secondary aerosols, their role in the mass transfer process cannot be accurately captured using traditional models, thus affecting the accuracy of simulation predictions.

[0005] Therefore, there is an urgent need for a method that combines experimental acquisition and model correction to model and correct parameters for secondary aerosol mass transfer behavior, so as to improve the rationality and predictive ability of source terms in numerical simulations, thereby providing theoretical support and modeling tools for solvent loss control and process optimization in organic amine carbon capture systems. Summary of the Invention

[0006] The purpose of the present invention is to overcome the problems of inaccurate secondary aerosol mass transfer modeling, large source term expression deviation, insufficient prediction ability, etc. in the existing organic amine carbon capture system, and to provide an aerosol multi-parameter dynamic monitoring system and a mass transfer source term correction method based on experiment-simulation fusion, which can accurately obtain the mass transfer coefficient of secondary aerosol and realize the dynamic correction of source term in numerical simulation, thereby improving the simulation accuracy and engineering adaptability of the carbon capture system in terms of absorbent loss and aerosol escape control.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] The first aspect of the present invention provides an aerosol multi-parameter dynamic monitoring system, comprising a condensation nucleus control device, a flue gas simulation device, and a multi-measuring point packed absorption tower, wherein specifically:

[0009] The condensation nucleus control device is used to generate monodisperse aerosol condensation nuclei with a single particle size;

[0010] The smoke simulation device is connected to the condensation nucleus control device and is used to generate simulated smoke with controllable components and flow rate based on the monodisperse aerosol condensation nuclei;

[0011] The air inlet of the multi-point packed absorption tower is connected to the flue gas simulation device, and at least three aerosol sampling points are spaced apart along the height of the tower. The multi-point packed absorption tower is used to allow the aerosol to contact the organic amine absorption liquid in countercurrent to produce a mass transfer reaction, and monitor the dynamic evolution of the aerosol concentration and particle size along the height of the tower through each sampling point;

[0012] The particle information collection system includes an isokinetic sampling gun and an ELPI measuring instrument. The isokinetic sampling gun is connected to each of the sampling measurement points, and the ELPI measuring instrument is connected to the isokinetic sampling gun. The ELPI measuring instrument is used to obtain aerosol concentration, particle size distribution, and residence time data in real time.

[0013] Furthermore, the condensation nucleus control device includes:

[0014] Monodisperse particle storage, which stores spherical particles with a particle size standard deviation of ≤5%;

[0015] a magnetic stirring container, connected to the monodisperse particle storage, for dispersing the monodisperse particles in deionized water to form a uniform suspension;

[0016] an ultrasonic atomizer, connected to the output end of the magnetic stirring container, for generating droplets with a particle size of 1-10 μm;

[0017] a silica gel drying tube connected to the output end of the ultrasonic atomizer, used for dehydrating the atomized droplets and outputting dry monodisperse condensation nuclei;

[0018] The premixing tank is connected to the output end of the silica gel drying tube and is used to uniformly mix the dried monodisperse condensation nuclei with the simulated smoke delivered by the smoke simulation device to form a simulated smoke environment with stable aerosol distribution.

[0019] Furthermore, the smoke simulation device includes:

[0020] Gas cylinder set, including CO2, N2, and O2 gas sources;

[0021] The mass flow meter group is installed on the gas delivery pipeline between the gas cylinder group and the premix tank, and is used to control the flow of each gas;

[0022] The heating belt is wrapped around the outer wall of the gas delivery pipeline used for aerosol delivery and is used to maintain the gas temperature at 40-60℃.

[0023] Furthermore, the arrangement of the sampling points of the multi-point packed absorption tower includes:

[0024] The first sampling point is 0.3-0.5m away from the upper surface of the tower bottom packing layer, and the distance between adjacent sampling points is 1 / 5-1 / 3 of the total height of the tower body, and they are arranged in sequence from bottom to top;

[0025] A dual sampling interface is provided at each sampling point, wherein the first interface is connected to the isokinetic sampling gun, and the second interface is provided with a pressure sensor, which is used to monitor the pressure difference fluctuation range in the tower in real time.

[0026] Furthermore, the isokinetic sampling gun is equipped with a PID controller, and based on the PID controller, the isokinetic sampling gun dynamically adjusts the pumping rate according to the flow rate in the tower;

[0027] The ELPI measuring instrument includes 12 particle size channels with a detection range of 0.03-10 μm and a sampling frequency of ≥1 Hz, where levels 1-3 correspond to ultrafine particles of 0.03-0.1 μm, levels 4-8 correspond to fine particles of 0.1-1 μm, and levels 9-12 correspond to coarse particles of 1-10 μm.

[0028] A second aspect of the present invention provides a mass transfer source term correction method based on experiment-simulation fusion, comprising the following steps:

[0029] S1. Experimental data acquisition:

[0030] Monodisperse aerosol condensation nuclei are generated by a condensation nucleus control device, mixed with simulated flue gas of controllable components and then introduced into a multi-measurement point packed absorption tower;

[0031] Multiple sampling points are set at intervals along the height direction of the multi-point packed absorption tower, and the aerosol concentration, particle size distribution, and residence time of each point are collected in real time through the particle information collection system;

[0032] S2. Calculation of experimental mass transfer coefficient:

[0033] The experimental mass transfer coefficient is calculated based on the concentration change, average particle surface area, and residence time difference at adjacent sampling points.

[0034] S3. Mass transfer source term correction:

[0035] An initial mass transfer source term model is constructed, in which the mass transfer coefficient is expressed by the correlation between the traditional Sherwood number and the Reynolds number and Schmidt number;

[0036] The experimental Sherwood number is inferred based on the experimental mass transfer coefficient, and the empirical parameters in the correlation equation are optimized using nonlinear regression to minimize the residual square sum between the experimental mass transfer coefficient and the predicted value of the optimized correlation equation, thereby obtaining a modified mass transfer source term model.

[0037] Furthermore, S2 specifically includes the following steps:

[0038] Dissolve monodisperse particles with a particle size standard deviation of ≤5% in deionized water and stir at 800-1200 rpm in a magnetic stirring vessel for 10-15 minutes to form a uniform suspension;

[0039] The suspension is atomized into droplets with a particle size of 1-10 μm by an ultrasonic atomizer, and dehydrated to a relative humidity of ≤5% by a silica gel drying tube to obtain dry monodisperse condensation nuclei;

[0040] The condensation nuclei were input into the premixing tank and mixed with the simulated flue gas. The initial concentration of condensation nuclei was 1×10 4 -5×10 5 particles / cm 3 .

[0041] Furthermore, in S1, the setting of the sampling points satisfies the following conditions:

[0042] The first sampling point is 0.3-0.5m away from the upper surface of the packing layer, and the distance between adjacent sampling points is 1 / 4-1 / 3 of the effective height of the absorption tower;

[0043] Each measuring point is equipped with a bidirectional sampling interface. The first interface is connected to a constant-speed sampling gun, and the second interface is installed with a pressure sensor. The pressure sensor is used to monitor the pressure difference fluctuation range in the tower in real time.

[0044] Furthermore, in S1, the specific process of generating monodisperse aerosol condensation nuclei includes:

[0045] Concentration change extraction: Based on the aerosol concentration data of adjacent measurement points recorded by the particle information acquisition system, the concentration change per unit volume between the two sampling points is calculated to reflect the total aerosol mass transfer;

[0046] Particle surface area calibration: Based on the particle size distribution data of each sampling point, the average surface area of the particle group is calculated using the spherical particle assumption model to characterize the scale of the aerosol mass transfer interface;

[0047] Dynamic time correlation and coefficient synthesis: The residence time difference of aerosol between adjacent measuring points is calculated by combining the local flow velocity in the absorption tower and the distance between measuring points. The ratio of the concentration change to the average surface area is divided by the residence time difference to finally obtain the experimental mass transfer coefficient.

[0048] Furthermore, S3 specifically includes the following steps:

[0049] An initial model of the mass transfer source term is established based on an empirical formula. The mass transfer coefficient is expressed as a dimensionless correlation between the Reynolds number of the fluid motion state and the Schmidt number of the material diffusion characteristic. An initial empirical parameter combination is defined as the starting point for optimization.

[0050] The dimensionless mass transfer correlation coefficient under actual working conditions is inferred from the experimental Sherwood number. The empirical parameters in the initial correlation formula are dynamically adjusted using a nonlinear regression algorithm to minimize the cumulative deviation between the experimental values and the model prediction values. Finally, a revised mass transfer source term model is output.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The monodisperse particle generation system of the present invention can control the concentration and size of the condensation nuclei entering the absorption tower to unify the size of the original particle size, and can more clearly obtain the particle size growth information caused by mass transfer at adjacent measuring points.

[0053] 2. The present invention directly obtains the mass transfer coefficient of secondary aerosol through experimental means, corrects the mass transfer coefficient expression based on the ideal liquid film or gas film assumption in the traditional model, and improves the accuracy of describing the actual mass transfer process of secondary aerosol.

[0054] 3. The source term correction model constructed in this invention takes into account key factors such as heterogeneous nucleation, agglomeration, local supersaturation and particle mass transfer driving force, breaking through the limitations of fixed source term expression in traditional CFD models and making the simulation results closer to the actual operating state. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the overall structure of the aerosol multi-parameter dynamic monitoring system of the present invention;

[0056] Figure 2 Schematic diagram of the structure of the condensation nucleus control device of the present invention;

[0057] Figure 3Schematic diagram of the structure of the smoke simulation device of the present invention;

[0058] Figure 4 This is a schematic diagram of the packed absorption tower 3 module with multiple measuring points of the present invention;

[0059] Figure 5 This is a schematic diagram of the particle information collection system 4 modules of the present invention;

[0060] In the picture:

[0061] 1.1. Monodisperse particles; 1.2. Magnetic stirring device; 1.3. Atomizer device; 1.4. Silica gel drying tube device; 1.5. Premix tank;

[0062] 2.1. Gas cylinder assembly; 2.2. Mass flow meter assembly; 2.3. Heating tape;

[0063] 3.1, particle sampling point;

[0064] 4.1. Isokinetic sampling gun; 4.2. ELPI measuring instrument. DETAILED DESCRIPTION

[0065] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, circuit structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0066] Example 1

[0067] The aerosol multi-parameter dynamic monitoring system in this embodiment includes a condensation nucleus control device 1, a smoke simulation device 2, and a multi-measurement point packed absorption tower 3. Figures 1 to 5 .

[0068] The condensation nucleus control device 1 is used to generate monodisperse aerosol condensation nuclei with a single particle size;

[0069] The smoke simulation device 2 is connected to the condensation nucleus control device 1 and is used to generate simulated smoke with controllable components and flow rate based on the monodisperse aerosol condensation nuclei;

[0070] The air inlet of the multi-measuring point packed absorption tower 3 is connected to the flue gas simulation device 2, and at least three aerosol sampling measurement points 3.1 are spaced apart along the height direction of the tower body. The multi-measuring point packed absorption tower 3 is used to make the aerosol and the organic amine absorption liquid contact in countercurrent to produce a mass transfer reaction, and monitor the dynamic evolution of the aerosol concentration and particle size along the height direction of the tower through each sampling measurement point 3.1.

[0071] The particle information collection system 4 includes an isokinetic sampling gun 4.1 and an ELPI measuring instrument 4.2. The isokinetic sampling gun 4.1 is connected to each of the sampling measurement points 3.1, and the ELPI measuring instrument 4.2 is connected to the isokinetic sampling gun 4.1. The ELPI measuring instrument 4.2 is used to obtain aerosol concentration, particle size distribution, and residence time data in real time.

[0072] The condensation nucleus control device 1 includes a monodisperse particle storage 1.1, a magnetic stirring container 1.2, an ultrasonic atomizer 1.3, a silica gel drying tube 1.4, and a premixing tank 1.5, wherein specifically: the monodisperse particle storage 1.1 stores spherical particles with a particle size standard deviation of ≤5%; the magnetic stirring container 1.2 is connected to the monodisperse particle storage 1.1, and is used to disperse the monodisperse particles in deionized water to form a uniform suspension; the ultrasonic atomizer 1.3 is connected to the output end of the magnetic stirring container 1.2, and is used to produce droplets with a particle size of 1-10 μm; the silica gel drying tube 1.4 is connected to the output end of the ultrasonic atomizer 1.3, and is used to dehydrate the atomized droplets and output dry monodisperse condensation nuclei; the premixing tank 1.5 is connected to the output end of the silica gel drying tube 1.4, and is used to uniformly mix the dried monodisperse condensation nuclei with the simulated flue gas delivered by the flue gas simulation device 2 to form a simulated flue gas environment with a stable aerosol distribution.

[0073] The smoke simulation device 2 includes a gas cylinder group 2.1, a mass flow meter group 2.2, and a heating belt 2.3. Specifically, the gas cylinder group 2.1 includes gas sources of CO2, N2, and O2; the mass flow meter group 2.2 is arranged on the gas delivery pipeline between the gas cylinder group 2.1 and the premixing tank 1.5, and is used to control the flow of each gas; the heating belt 2.3 is wrapped around the outer wall of the gas delivery pipeline used for aerosol delivery, and is used to maintain the gas temperature at 40-60°C.

[0074] The isokinetic sampling gun 4.1 is equipped with a PID controller, based on which the isokinetic sampling gun 4.1 dynamically adjusts the pumping rate according to the flow rate in the tower;

[0075] The ELPI measuring instrument 4.2 contains 12 particle size channels with a detection range of 0.03-10μm and a sampling frequency of ≥1Hz. Levels 1-3 correspond to ultrafine particles of 0.03-0.1μm, levels 4-8 correspond to fine particles of 0.1-1μm, and levels 9-12 correspond to coarse particles of 1-10μm.

[0076] During specific implementation, the multi-measuring point absorption tower 3 is a packed tower.

[0077] The top of the multi-measuring point absorption tower 3 is provided with an organic amine absorbent liquid inlet and a purified gas outlet; the bottom of the multi-measuring point absorption tower 3 is provided with a simulated flue gas injection port and a rich amine liquid outlet.

[0078] Example 2

[0079] The core principle of this embodiment is: generating monodisperse particles through the condensation nucleus control device 1 and simulating a real flue gas environment, combining the dynamic monitoring data of aerosol size / concentration in the multi-measurement point packed absorption tower, and inverting to obtain the experimental mass transfer coefficient (Kexp) of the secondary aerosol along the tower height; based on this, the Sherwood number correlation in the CFD model is reconstructed, and the empirical parameters of the traditional mass transfer source term are optimized using nonlinear regression, and the static Ranz-Marshall formula is upgraded to a dynamic calibration model, so that the numerical simulation can accurately quantify the coupling effect of heterogeneous nucleation and agglomeration effects on aerosol mass transfer growth, and ultimately achieve the prediction error of aerosol escape amount of the amine carbon capture system to be reduced to less than 10%, providing a high-precision digital tool for solvent loss control.

[0080] The mass transfer source term correction method based on experiment-simulation fusion in this embodiment includes the following steps:

[0081] S1. Experimental data acquisition:

[0082] Monodisperse aerosol condensation nuclei are generated by the condensation nucleus control device 1, mixed with simulated flue gas of controllable components and then introduced into the multi-measuring point packed absorption tower 3;

[0083] Multiple sampling points 3.1 are set at intervals along the height direction of the multi-point packed absorption tower 3, and the aerosol concentration, particle size distribution, and residence time of each sampling point are collected in real time by the particle information collection system 4;

[0084] In S1, the setting of the sampling measurement point 3.1 meets the following conditions:

[0085] The first sampling point is 0.3-0.5m away from the upper surface of the packing layer, and the distance between adjacent sampling points is 1 / 4-1 / 3 of the effective height of the absorption tower;

[0086] Each measuring point is equipped with a bidirectional sampling interface, the first interface is connected to the isokinetic sampling gun 4.1, and the second interface is installed with a pressure sensor, which is used to monitor the pressure difference fluctuation range in the tower in real time.

[0087] In specific implementation, in S1, the specific process of generating monodisperse aerosol condensation nuclei includes:

[0088] Dissolve monodisperse particles 1.1 with a particle size standard deviation of ≤5% in deionized water, and stir at 800-1200 rpm for 10-15 minutes in a magnetic stirring vessel 1.2 to form a uniform suspension;

[0089] The suspension is atomized into droplets with a particle size of 1-10 μm by an ultrasonic atomizer 1.3, and dehydrated to a relative humidity of ≤5% by a silica gel drying tube 1.4 to obtain dry monodisperse condensation nuclei;

[0090] The condensation nuclei were input into the premixing tank 1.5 and mixed with the simulated flue gas. The initial concentration of condensation nuclei was 1×10 4 -5×10 5 particles / cm 3 .

[0091] S2. Calculation of experimental mass transfer coefficient:

[0092] The experimental mass transfer coefficient is calculated based on the concentration change, average particle surface area, and residence time difference between adjacent sampling points 3.1.

[0093] In specific implementation, S2 specifically includes the following steps:

[0094] Concentration change extraction: Based on the aerosol concentration data of adjacent measurement points recorded by the particle information acquisition system 4, the concentration change per unit volume between the two sampling measurement points 3.1 is calculated to reflect the total aerosol mass transfer;

[0095] Particle surface area calibration: Based on the particle size distribution data of 3.1 at each sampling point, the average surface area of the particle group is calculated using the spherical particle assumption model to characterize the scale of the aerosol mass transfer interface;

[0096] Dynamic time correlation and coefficient synthesis: The residence time difference of aerosol between adjacent measuring points is calculated by combining the local flow velocity in the absorption tower and the distance between measuring points. The ratio of the concentration change to the average surface area is divided by the residence time difference to finally obtain the experimental mass transfer coefficient.

[0097] S3. Mass transfer source term correction:

[0098] An initial mass transfer source term model is constructed, in which the mass transfer coefficient is expressed by the correlation between the traditional Sherwood number and the Reynolds number and Schmidt number;

[0099] The experimental Sherwood number is inferred based on the experimental mass transfer coefficient, and the empirical parameters in the correlation equation are optimized using nonlinear regression to minimize the residual square sum between the experimental mass transfer coefficient and the predicted value of the optimized correlation equation, thereby obtaining a modified mass transfer source term model.

[0100] In specific implementation, S3 includes the following steps:

[0101] An initial model of the mass transfer source term is established based on an empirical formula. The mass transfer coefficient is expressed as a dimensionless correlation between the Reynolds number of the fluid motion state and the Schmidt number of the material diffusion characteristic. An initial empirical parameter combination is defined as the starting point for optimization.

[0102] The dimensionless mass transfer correlation coefficient under actual working conditions is inferred from the experimental Sherwood number. The empirical parameters in the initial correlation formula are dynamically adjusted using a nonlinear regression algorithm to minimize the cumulative deviation between the experimental values and the model prediction values. Finally, a revised mass transfer source term model is output.

[0103] In specific implementations, the particle information collection system 4 and the isokinetic sampling gun 4.1 can match the gas velocity of the ELPI measuring instrument 4.2, extracting the aerosol from the absorption tower at a uniform rate. The particle information collection system 4 collects aerosol information from multiple measuring points 3 on the packed absorption tower, obtaining the particle size Di, particle concentration Ci, and average particle surface area A at each measuring point. The residence time Δti of adjacent measuring points is then calculated based on the upward distance. Based on the changes in the particle surface flux, the true mass transfer coefficient Kexp of the amine aerosol in the absorption tower along the rising flue gas direction is derived (as shown in Eq. 1).

[0104]

[0105] where ΔC i is the amine vapor concentration difference between adjacent measuring points (i-th measuring point and i+1-th measuring point), and dm is the mass change absorbed or released by aerosol particles through the mass transfer process within the time interval dt.

[0106] In numerical simulation, the mass transfer process and particle growth of the multiphase system based on the Euler-Euler model are described by the change of the mass transfer source term. As shown in Eq. 2, the mass transfer source term m i,source Describes the mass exchange between phases:

[0107] m i,source =K cfd ·(C & -C eq )·S Eq.2

[0108] S is the contact area between phases per unit volume, C & and C eq Represent the far-field concentration and surface equilibrium concentration during the mass transfer process, respectively, and can be obtained through simulation calculations. The mass transfer coefficient Kcfd and Shcfd (Sherwood number) have the following mathematical relationship (see Eq. 3):

[0109] K cfd =(S hcfd ·D gas ) / D p Eq.3

[0110] Gas phase diffusion coefficient D gas and particle size D pis a known number. Shcfd is considered to be related to the Reynolds number (Re) and the Schmidt number (Sc). The specific form is referred to Eq.4:

[0111]

[0112] a corr 、b corr 、c corr d corr It is an empirical parameter and needs to be determined through experiments. In the above experiment, after obtaining the true mass transfer coefficient Kexp, the experimental Sherwood number (Sh exp ), through the nonlinear regression method of Eq.5, we can obtain empirical parameter values that are highly consistent with the experimental data (with small residuals), and bring them into Shcfd to complete the numerical simulation of mass transfer source terms and mass transfer coefficients.

[0113]

[0114] The above description of the embodiments is intended to facilitate understanding and use of the invention by those skilled in the art. It will be apparent that those skilled in the art can readily make various modifications to these embodiments and apply the general principles described herein to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above-described embodiments. Improvements and modifications made by those skilled in the art based on the disclosure of the present invention, without departing from the scope of the present invention, should be within the scope of protection of the present invention.

Claims

1. An aerosol multi-parameter dynamic monitoring system, characterized in that: include: A condensation nucleus control device (1) is used to generate monodisperse aerosol condensation nuclei with a single particle size; A smoke simulation device (2) is connected to the condensation nucleus control device (1) and is used to generate simulated smoke with controllable components and flow rate based on the monodisperse aerosol condensation nuclei; A multi-measuring point packed absorption tower (3) has an air inlet connected to the flue gas simulation device (2), and at least three aerosol sampling measuring points (3.1) are provided at intervals along the height direction of the tower body. The multi-measuring point packed absorption tower (3) is used to allow the aerosol to contact with the organic amine absorption liquid in countercurrent to generate a mass transfer reaction, and monitor the dynamic evolution of the concentration and particle size of the aerosol along the height direction of the tower through each sampling measuring point (3.1); The particle information collection system (4) comprises an isokinetic sampling gun (4.1) and an ELPI measuring instrument (4.2), wherein the isokinetic sampling gun (4.1) is connected to each of the sampling measurement points (3.1), and the ELPI measuring instrument (4.2) is connected to the isokinetic sampling gun (4.1), and the ELPI measuring instrument (4.2) is used to obtain aerosol concentration, particle size distribution, and residence time data in real time.

2. The aerosol multi-parameter dynamic monitoring system according to claim 1, characterized in that: The condensation nucleus control device (1) comprises: A monodisperse particle reservoir (1.1) storing spherical particles with a particle size standard deviation of ≤5%; a magnetic stirring container (1.2), connected to the monodisperse particle storage (1.1), for dispersing the monodisperse particles in deionized water to form a uniform suspension; An ultrasonic atomizer (1.3), connected to the output end of the magnetic stirring container (1.2), is used to generate droplets with a particle size of 1-10 μm; A silica gel drying tube (1.4) is connected to the output end of the ultrasonic atomizer (1.3) and is used to dehydrate the atomized droplets and output dry monodisperse condensation nuclei; The premixing tank (1.5) is connected to the output end of the silica gel drying tube (1.4) and is used to uniformly mix the dried monodisperse condensation nuclei with the simulated smoke delivered by the smoke simulation device (2) to form a simulated smoke environment with a stable aerosol distribution.

3. The aerosol multi-parameter dynamic monitoring system according to claim 1, characterized in that: The smoke simulation device (2) comprises: Gas cylinder set (2.1), including gas sources of CO2, N2, and O2; A mass flow meter group (2.2) is provided on the gas delivery pipeline between the gas cylinder group (2.1) and the premix tank (1.5) and is used for flow control of each gas; The heating belt (2.3) is wrapped around the outer wall of the gas delivery pipeline for aerosol delivery and is used to maintain the gas temperature at 40-60°C.

4. The aerosol multi-parameter dynamic monitoring system according to claim 1, characterized in that: The arrangement of the sampling measurement points (3.1) of the multi-measurement point packed absorption tower (3) includes: The first sampling point is 0.3-0.5m away from the upper surface of the tower bottom packing layer, and the distance between adjacent sampling points is 1 / 5-1 / 3 of the total height of the tower body, and they are arranged in sequence from bottom to top; Each sampling point (3.1) is provided with a double sampling interface, wherein the first interface is connected to the isokinetic sampling gun (4.1), and the second interface is provided with a pressure sensor, which is used to monitor the pressure difference fluctuation range in the tower in real time.

5. The aerosol multi-parameter dynamic monitoring system according to claim 1, characterized in that: The isokinetic sampling gun (4.1) is equipped with a PID controller, and based on the PID controller, the isokinetic sampling gun (4.1) dynamically adjusts the pumping rate according to the flow rate in the tower; The ELPI measuring instrument (4.2) comprises 12 particle size channels with a detection range of 0.03-10 μm and a sampling frequency of ≥1 Hz, wherein levels 1-3 correspond to ultrafine particles of 0.03-0.1 μm, levels 4-8 correspond to fine particles of 0.1-1 μm, and levels 9-12 correspond to coarse particles of 1-10 μm.

6. A mass transfer source term correction method based on experiment-simulation fusion, characterized in that: The following steps are involved: S1. Experimental data acquisition: Monodisperse aerosol condensation nuclei are generated by a condensation nucleus control device (1), mixed with simulated flue gas of controllable components, and then introduced into a multi-measuring point packed absorption tower (3); A plurality of sampling measurement points (3.1) are arranged at intervals along the height direction of the multi-measurement point packed absorption tower (3), and the aerosol concentration, particle size distribution, and residence time of each measurement point are collected in real time by a particle information collection system (4); S2. Calculation of experimental mass transfer coefficient: The experimental mass transfer coefficient is calculated based on the concentration change, average particle surface area, and residence time difference at adjacent sampling points (3.1); S3. Mass transfer source term correction: An initial mass transfer source term model is constructed, in which the mass transfer coefficient is expressed by the correlation between the traditional Sherwood number and the Reynolds number and Schmidt number; The experimental Sherwood number is inferred based on the experimental mass transfer coefficient, and the empirical parameters in the correlation equation are optimized using nonlinear regression to minimize the residual square sum between the experimental mass transfer coefficient and the predicted value of the optimized correlation equation, thereby obtaining a modified mass transfer source term model.

7. The mass transfer source term correction method based on experiment-simulation fusion according to claim 6, characterized in that: In S1, the specific process of generating monodisperse aerosol condensation nuclei includes: Dissolve monodisperse particles (1.1) with a particle size standard deviation of ≤5% in deionized water and stir at 800-1200 rpm in a magnetic stirring vessel (1.2) for 10-15 minutes to form a uniform suspension; The suspension is atomized into droplets with a particle size of 1-10 μm by an ultrasonic atomizer (1.3), and dehydrated to a relative humidity of ≤5% by a silica gel drying tube (1.4) to obtain dry monodisperse condensation nuclei; The condensation nuclei were input into the premixing tank (1.5) and mixed with the simulated flue gas. The initial concentration of condensation nuclei was 1×10 4 -5×10 5 particles / cm 3 .

8. The mass transfer source term correction method based on experiment-simulation fusion according to claim 6 is characterized in that: In S1, the setting of the sampling measurement point (3.1) meets the following conditions: The first sampling point is 0.3-0.5m away from the upper surface of the packing layer, and the distance between adjacent sampling points is 1 / 4-1 / 3 of the effective height of the absorption tower; Each measuring point is equipped with a bidirectional sampling interface, the first interface is connected to a constant speed sampling gun (4.1), and the second interface is equipped with a pressure sensor, which is used to monitor the pressure difference fluctuation range in the tower in real time.

9. The mass transfer source term correction method based on experiment-simulation fusion according to claim 6, characterized in that: S2 specifically includes the following steps: Concentration change extraction: Based on the aerosol concentration data of adjacent measurement points recorded by the particle information acquisition system (4), the concentration change per unit volume between the two sampling measurement points (3.1) is calculated to reflect the total aerosol mass transfer; Particle surface area calibration: Based on the particle size distribution data of each sampling point (3.1), the average surface area of the particle group is calculated using the spherical particle assumption model to characterize the scale of the aerosol mass transfer interface; Dynamic time correlation and coefficient synthesis: The residence time difference of aerosol between adjacent measuring points is calculated by combining the local flow velocity in the absorption tower and the distance between measuring points. The ratio of the concentration change to the average surface area is divided by the residence time difference to finally obtain the experimental mass transfer coefficient.

10. The mass transfer source term correction method based on experiment-simulation fusion according to claim 6, characterized in that: In S3, the following steps are specifically included: An initial model of the mass transfer source term is established based on an empirical formula. The mass transfer coefficient is expressed as a dimensionless correlation between the Reynolds number of the fluid motion state and the Schmidt number of the material diffusion characteristic. An initial empirical parameter combination is defined as the starting point for optimization. The dimensionless mass transfer correlation coefficient under actual working conditions is inferred from the experimental Sherwood number. The empirical parameters in the initial correlation formula are dynamically adjusted using a nonlinear regression algorithm to minimize the cumulative deviation between the experimental values and the model prediction values. Finally, a revised mass transfer source term model is output.

Images